ParticleFilterResults

This archive contains the BEAST 2 XML and R scripts necessary to reproduce the results presented in a manuscript which is currently in preparation. Note that besides BEAST 2 itself, all XML files require the BEAST 2 package EpiInf to be installed. Installation instructions can be found on the project web page at https://tgvaughan.github.io/EpiInf.

The files are as follows:

• validation_likelihood/

  • BD_DensityMap_r.xml:

    Simulates (1) an epidemic trajectory under the linear birth-death model with an explicit sampling process and (2) a sampled transmission tree compatible with this trajectory. The XML then uses the algorithm presented in the paper to estimate the likelihood for various values of the removal-on-sampling parameter "r". The likelihood values are also computed using an analytical solution. Both sets of values are reported in the output. This script was used to produce the comparison shown in figure 2a of the manuscript.

  • SIS_DensityMap_beta.xml:

    Simulates (1) an epidemic trajectory under the nonlinear SIS model with an explicit sampling process and (2) a sampled transmission tree compatible with this trajectory. The XML then uses the algorithm presented in the paper to estimate the likelihood for various values of the infection rate parameter "beta". This script was used to produce the comparison shown in figure 2b of the manuscript.

• validation_incidence/

  • BD_traj_simulator.xml:

    Simulates an epidemic trajectory under the linear birth-death model with an explicit sampling model. The simulated trajectory is saved in a file named "simulated.traj" in the current directory.

  • BD_inference.xml:

    When run using BEAST from the command line with the following arguments:

       beast -D leaf_frac=0.1 BD_inference.xml
    

    from a directory containing a simulated trajectory file named "simulated.traj", this script will generate samples from the posterior over the infection rate parameter "beta" with only 10% of the samples in the simulated trajectory corresponding to sampled phylogeny leaves, and the remaining corresponding to unsequenced samples.

    Running the same command but varying the value of leaf_frac will repeat the analysis with a varying fraction of samples assigned to tree leaves.

    Comparing the posterior distributions for "beta" from each such analysis will produce the results shown in figure 3 in the manuscript.

• simulated_inference/

  • BD_demo.xml: Simulates (1) an epidemic trajectory under the linear birth-death model with an explicit sampling process, (2) a sampled transmission tree compatible with this trajectory, and (3) a molecular sequence alignment by evolving a random ancestral sequence down this simulated tree. The XML then uses the algorithm presented in the manuscript to jointly infer the sampled tree, epidemic trajectory and model parameters from the simulated sequence alignment. This script was used to produce the results shown in figure 4a.

  • SIS_demo.xml:

    Performs exactly the same sequence of operations as BD_demo.xml, but under the nonlinear SIS model. This script was used to produce the results shown in figure 4b.

  • SIR_demo.xml:

    Performs exactly the same sequence of operations as BD_demo.xml, but under the nonlinear SIR model. This script was used to produce the results shown in figure 4c.

• validation_trajectory/

  • BD_psiSamp_inference.xml:

    Simulates (1) an epidemic trajectory under the linear birth-death model with an explicit sampling process, (2) a sampled transmission tree compatible with this trajectory, and (3) a molecular sequence alignment by evolving a random ancestral sequence down the simulated tree. The XML then uses the algorithm presented in the manuscript to jointly infer the sampled tree and the epidemic trajectory, conditional on the true model parameters.

  • SIS_psiSamp_inference.xml:

    Performs exactly the same sequence of operations as BD_psiSamp_inference.xml, but under the nonlinear SIS model.

  • SIR_psiSamp_inference.xml:

    Performs exactly the same sequence of operations as BD_psiSamp_inference.xml, but under the nonlinear SIR model.

  • analyze_wc_results.R:

    Assuming that 200 of repetitions of the above analyses have been run with seeds 1 through 200 and that the output of these analyses has been placed in a subdirectory named "results/", this R script will summarise the trajectory inference results and save this summary to files named "resultsBD.txt", "resultsSIS.txt" and "resultsSIR.txt" in the current directory.

  • plot_wc_results.R:

    Assuming the results files generated by "analyze_wc_results.R" are in the current directory, this R script will produce the graphs shown as figure 5 in the manuscript.

• ebola/

  • kailahun_seq+incidence.xml:

    Running this script will perform the Ebola virus analysis incorporating both genomic data and case count data whose results are presented in Figure 6 and Table 2 of the manuscript.

  • kailahun_seqonly_4weeks.xml:

    Running this script will perform the Ebola virus analysis incorporating only genomic data collected during the first 4 weeks of sampling as presented in Figure 6 of the manuscript.